Abstract

A system for determining a prescriptive therapy instruction set may include a computing device configured to receive a first subject prescription datum associated with a first subject; receive a first subject tolerance datum associated with the first subject; determine a prescriptive therapy instruction set by training a prescriptive therapy instruction set machine learning model on a training dataset including a plurality of example subject prescription data and subject tolerance data as inputs correlated to a plurality of example prescriptive therapy instruction sets as outputs; and generating the prescriptive therapy instruction set as a function of the subject prescription datum and the subject tolerance datum using the trained prescriptive therapy instruction set machine learning model; receive a second subject tolerance datum associated with the first subject; and retrain the prescriptive therapy instruction set machine learning model as a function of the second subject tolerance datum.

IPC Classes  ?

• G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
• G06N 20/00 - Machine learning
• G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
• G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• G16H 70/40 - ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage

Abstract

A system for causative chaining of prognostic label classifications includes a classification device configured to receive training data including a plurality of first data entries, each including at least a first element of physiological state data and at least a correlated first prognostic label and a plurality of second data entries, each including at least a second prognostic label and at least a correlated third prognostic label, and to record at least a first biological extraction. The system includes a prognostic label learner configured to generate at least a first prognostic output as a function of the first training set and the at least a physiological test sample, and a causal link learner configured to generate at least a second prognostic output causally linked to the first prognostic output as a function of the second training set and the at least a first prognostic output.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06F 16/906 - ClusteringClassification
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
• G16H 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies

Abstract

A blood collection device including an array of needles configured to pierce an epidermal layer and extract a blood sample of a user, a collection channel positioned proximal to the array of needles and configured to transport the blood sample from the array of needles and a collection container configured to store the blood sample, wherein the collection container includes an inlet, wherein the inlet is configured to receive the blood sample and an outlet wherein the outlet is configured to expel the blood sample from the collection container.

IPC Classes  ?

• A61B 5/15 - Devices for taking samples of blood

Abstract

A system for confirming an advisory interaction with an artificial intelligence platform. The system includes a constitutional generator module configured to receive a first advisory input, retrieve an expert input, select a machine-learning process as a function of the expert input, and generate a therapeutic corrector. The system includes a constitutional advisory module configured to display a therapeutic corrector on a graphical user interface and receive a second advisory input. The system includes a best practices module the best practices module designed and configured to retrieve from an expert database a best practices training set, calculate an optimal vector output, generate an optimal vector output containing an expected therapeutic corrector implementation response, authenticate a second advisory input, and update the best practices module.

IPC Classes  ?

• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G06N 20/00 - Machine learning
• G16H 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines

Abstract

A system for generating a cancer alleviation nourishment plan, the system including a computing device comprising at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring processor to receive at least a biomarker datum relating to a user; generate a nourishment plan as a function of the at least a biomarker datum; integrate the nourishment plan with an external platform, wherein integrating the nourishment plan includes: generating and displaying a first window comprising the nourishment plan within a graphical user interface of the external platform; generating and displaying a data prompt within the first window as a function of the nourishment plan; generate and execute a feedback signal within a second window on the external platform as a function of the nourishment score; and automatically modify the first window as a function of the user actions.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets

Abstract

An apparatus for generating cryptographic keys associated with a biological extraction is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a plurality of biological extractions from a plurality of users. The memory instructs the processor to classify each of the plurality of biological extractions to a plurality of biological extraction clusters. The memory instructs the processor to generate an interconnection metric as a function of a comparison between the plurality of biological extraction clusters. The memory instructs the processor to generate an interconnection metric as a function of a comparison between the plurality of biological extraction clusters using a metric machine learning model. The memory instructs the processor to generate a cluster key associated with the interconnection metric and a biological extraction cluster of the plurality of biological extraction clusters.

IPC Classes  ?

• H04L 9/08 - Key distribution
• G06F 18/23 - Clustering techniques

Abstract

In an aspect, a system for generating lifestyle change recommendations based on biological extractions includes a computing device designed and configured for receiving a biological extraction pertaining to a user generating, using a first machine-learning process, a plurality of lifestyle intervention combinations as a function of the biological extraction, assigning, to each lifestyle intervention combination of the plurality of lifestyle intervention combinations, a degree of projected user adherence to the lifestyle intervention combination, wherein assigning further comprises performing a second machine learning process, and selecting, from the plurality of lifestyle intervention combinations, a lifestyle intervention combination as a function of the degree of projected user adherence of the selected lifestyle intervention combination.

IPC Classes  ?

• G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets

Abstract

An apparatus for physiologically informed gestational inquiries is disclosed. The apparatus includes at least a processor and memory communicatively connected to the at least a processor. The memory instructs the processor to receive a biological extraction from the user. The memory instructs the processor to receive a gestational inquiry from the user. The memory instructs the processor to separate the gestational inquiry from a description of the gestational inquiry. The memory instructs the processor to determine a gestational target as a function of the gestational inquiry and the biological extraction. The memory instructs the processor to generate a gestational report as a function of the gestational target.

IPC Classes  ?

• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning
• G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

The present disclosure is generally directed to an apparatus for using a feedback loop to optimize meals, may include at least a processor; and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to retrieve nutrition data from a database. The processor may be configured to generate an optimization score, wherein generating the optimization score may include training an optimization machine-learning model, wherein the optimization machine-learning model is trained with optimization training data, inputting a nutrient quantity to the optimization machine-learning model to output a target nutrient score, and generating an optimization score as a function of the nutrition data and the target nutrient score.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06F 40/40 - Processing or translation of natural language

Abstract

A system for generating a nourishment program includes a computing device configured to retrieve a nociception parameter, classify the nociception parameter to a nociception grouping, identify, using the nociception grouping, a plurality of nutrition elements, wherein identifying the plurality of nutrition elements includes generating a plurality of nutritional metrics associated with reduction of nociception as a function of the nociception grouping, determining a respective effect of each nutritional metric of the plurality of nutritional metrics on the nociception parameter, calculating at least a nutritional level as a function of the respective effect of each nutritional metric, wherein the at least a nutritional level comprises an amount intended to address the nociception parameter, and identifying the plurality of nutrition elements as a function of the at least a nutritional level, and generate a nociception nourishment program using the plurality of nutrition elements.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets

Abstract

A system for optimizing supplement decisions is disclosed. The system includes a computing device configured to receive a longevity inquiry from a remote device. The system retrieves a biological extraction pertaining to a user and identifies a longevity element associated with a user. The system selects an ADME model utilizing a biological extraction. The system generates a machine-learning algorithm utilizing the selected ADME model to input a longevity element associated with a user as an input and output an ADME factor. The system identifies a second longevity element compatible with the ADME factor as a function of the first longevity element. The system selects the second longevity element as a tolerant longevity element. A method for optimizing supplement decisions is also disclosed.

IPC Classes  ?

• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G06N 20/00 - Machine learning
• G16B 40/30 - Unsupervised data analysis
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets

Abstract

A system for generating a lifestyle-based disease prevention plan, the system including a computing device configured to receive at least a user biomarker input, produce a user profile as a function of the at least a user biomarker input, and generate a lifestyle-based disease prevention plan as a function of the user profile including training a machine learning process with a lifestyle training data set where the lifestyle training data set further comprises lifestyle elements correlated to a plurality of outputs containing diseases prevented and producing the lifestyle-based disease prevention plan as a function of the user profile and machine learning process.

IPC Classes  ?

• G16H 20/70 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• G06F 16/3331 - Query processing
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/08 - Learning methods
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies

Abstract

An apparatus for resource guidance, wherein the apparatus comprises at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory containing instructions configuring the at least a processor to receive a request for a resource containing a desired allocation; identify a first nourishment provider for a first resource; label the first resource as a function of the desired allocation; identify a second nourishment provider for a second resource; label the second resource as a function of the first nourishment resource and the desired allocation; receive a depot label from the first nourishment provider and the second nourishment provider; aggregate the first resource and the second resource as a function of the depot label; and generate resource guidance using the depot label.

IPC Classes  ?

• G06F 9/46 - Multiprogramming arrangements
• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets

Abstract

A system and method for generating a customized badge is disclosed. The system comprises at least a processor and a memory to receive cohort data, receive alimentary array data, generate a cohort digital badge for the alimentary item of at least alimentary array data as a function of the cohort data, receive user data wherein the user data comprises biological extraction data, update the cohort digital badge to a user digital badge as a function of the biological extraction data, and display the user digital badge.

IPC Classes  ?

• G06Q 30/0601 - Electronic shopping [e-shopping]

Abstract

An apparatus and method for generating an ingredient chain, comprising at least a processor; and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to receive recipe data from a user; extract, from the recipe data, a plurality of ingredients; classify, utilizing an ingredient classifier, the plurality of ingredients to output a score; and generate, as a function of the score, a first ingredient chain for the user.

IPC Classes  ?

• G06F 16/35 - ClusteringClassification
• G06F 16/335 - Filtering based on additional data, e.g. user or group profiles
• G06F 16/338 - Presentation of query results

Abstract

A system for additive manufacturing of nutritional supplement servings, the system comprising a computing device configured to receive a plurality of nutritional needs of a user and a nutrient plan; determine a nutritional input to the user as a function of the nutrient plan; detect at least a nutrition deficiency as a function of the plurality of nutritional needs, the nutritional input and the nutrient plan; calculate at least a supplement dose; select an ingredient combination as a function of the at least a supplement dose, and selecting an ingredient combination including at least a substrate ingredient and at least a supplement ingredient as a function of the nutritional deficiency and the at least a delivery vehicle; and initiate manufacture of a nutritional supplement serving at the additive manufacturing device.

IPC Classes  ?

• A23P 20/25 - Filling or stuffing cored food pieces, e.g. combined with coring or making cavities
• A23L 33/00 - Modifying nutritive qualities of foodsDietetic productsPreparation or treatment thereof

Abstract

A method and system of achieving vibrant constitution based on user inputs, the method comprising receiving by at least a processor training data including a plurality of data entries, wherein each data entry of the plurality of data entries includes at least an element of physiological state data and at least a correlated alimentary instruction set; recording at least a biological extraction from a user; receiving at least a user input relating to an alimentary standard; and generating at least an alimentary instruction set as a function of the at least a biological extraction and the at least a user input.

IPC Classes  ?

• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G06N 20/00 - Machine learning
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Abstract

An apparatus for geographically tracking nourishment selection, the apparatus comprising at least a computing device and a memory configured to receive a location associated with a user from a user device; determine a nourishment provider within the location, wherein the nourishment provider is associated with a plurality of nourishment possibilities; locate a performance character associated with the user, wherein the performance character contains a nourishment score; generate a user profile wherein the user profile comprises the location, the nourishment provider, and the performance character; grade, using a machine-learning process, the plurality of nourishment possibilities associated with the nourishment provider using a plurality of corresponding nourishment possibility indexes; and a graphical user interface configured to display the plurality of graded nourishment possibilities and wherein the graphical user interface is configured to receive an input from the user relating to the graded nourishment possibilities.

IPC Classes  ?

• G06Q 30/0601 - Electronic shopping [e-shopping]
• G01C 21/36 - Input/output arrangements for on-board computers
• G06N 20/00 - Machine learning
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• H04L 67/306 - User profiles
• H04W 4/02 - Services making use of location information
• H04W 4/029 - Location-based management or tracking services

Abstract

A system for generating a virtual assistant in a messaging user interface, the system comprising a computing device configured to receive, from a sensor, a change in the biological state of a plurality of users; prioritize an agenda action relating to food delivery of a plurality of agenda actions contained within a user agenda list as a function of the change in the biological state of the plurality of users; initiate a virtual message user interface between a user client device and the computing device; select a conversation profile for the virtual assistant, wherein the conversation profile identifies instructions relating to the food delivery; generate a communication to the user comprising the instructions relating to the food delivery; identify a third-party device as a function of the instructions relating to the food delivery; share information with the third-party device as a function of the instructions relating to the food delivery.

IPC Classes  ?

• H04L 51/18 - Commands or executable codes
• G06F 16/31 - IndexingData structures thereforStorage structures
• G06N 3/006 - Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
• H04L 51/046 - Interoperability with other network applications or services

Abstract

A system for constitutional analysis using objective functions, the system comprising a computing device configured to generate a ranked list of diseases, comprising determining a plurality of disease impact score vectors associated with a plurality of diseases; generating a first objective function; and ranking the diseases; receive, from a user, a plurality of user physiological history data, wherein the user physiological history data was collected by a wearable device; identify, as a function of a disease state classifier, a plurality of disease states associated with the plurality of user physiological history data; match at least a disease state of the plurality of disease states to the ranked list of diseases; and generate a curative habitual pattern to alleviate the at least a disease state, wherein the curative habitual pattern contains a nutrition pattern containing a nutrition target for each eating occasion contained within the nutrition pattern.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment

Abstract

A system and method for biologically determined artificial intelligence selection guidance, the system comprising a computing device designed and configured to receive at least a biological extraction and an item descriptor from a user; identify, a user set identifier matching the user; produce a selection guidance using the user set identifier and the item descriptor; determine whether the item descriptor is suitable for the user; block a different item of the selection guidance as a function of determining the item descriptor is not suitable for the user; and provide the selection guidance to the user.

IPC Classes  ?

• G06N 3/08 - Learning methods

Abstract

A system for displaying refreshment outlooks, the system comprising a computing device, the computing device designed and configured to retrieve a user profile from a profile database; determine a refreshment position using the user profile; select current refreshment possibilities contained within the refreshment position; output a refreshment target, using the user profile; examine current refreshment possibilities and the refreshment target; and generate a replacement for each of the current refreshment possibilities to optimize the nutritional content of each current refreshment possibilities as a function of the refreshment target and the user profile.

IPC Classes  ?

• G06Q 30/0601 - Electronic shopping [e-shopping]
• G06F 16/9535 - Search customisation based on user profiles and personalisation
• G06Q 30/0251 - Targeted advertisements

Abstract

A system for generating a program for addressing hematological disorders using machine-learning, the system comprising a computing device configured to acquire at least a hematological datum relating to a subject; retrieve a hematological profile related to the subject as a function of the at least a hematological datum; classify the hematological profile to a hematological disorder bundle; determine, using the hematological disorder bundle and the hematological profile, at least a nutritional level; identify, using the at least a nutritional level, at least a nutrition element and a behavior pattern; and generate a consumption program based on the at least a nutrition element and a behavior pattern.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment

Abstract

A system for a configurable device environment, the system comprising a computing device configured to receive remote data corresponding to a subject and a plurality of signals from at least a sensor proximate to the subject, retrieve a biometric profile of the subject, identify a pattern of accessory device states for a plurality of accessory devices, wherein identifying includes determining a coordinated state change for a group of accessory devices of the plurality of accessory devices as a function of the remote data and the biometric profile and identifying the pattern of accessory device states as a function of the coordinated state change, determine an automation rule for the group of accessory devices as a function of the pattern of accessory device states, and transmit, to the group of accessory devices, the automation rule.

IPC Classes  ?

• G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric

Abstract

A system for generating an alimentary instruction set, the system comprising a computing device; a diagnostic engine operating on the computing device, wherein the diagnostic engine is configured to assemble a first training set, the first training set comprising a plurality of diagnostic outputs describing a plurality of health conditions and a plurality of correlated alimentary instruction sets; parse the first training set into at least a vector; train, using the at least a vector a machine learning model; receive an input to the trained machine learning model containing a diagnostic output; and generate an output to the trained machine learning model containing an alimentary instruction set.

IPC Classes  ?

• G16H 50/00 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
• G06N 5/022 - Knowledge engineeringKnowledge acquisition
• G06N 20/00 - Machine learning
• G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
• G06N 20/20 - Ensemble learning
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Abstract

A system and method for initiating manufacturing of an edible combination includes a computing device, the computing device configured to identify at least a biochemical profile as a function of a first biological extraction, generate at least a new profile as a function of a second biological extraction, produce at least a self-based nutritional program, wherein producing further comprises determining at least a corporeal variation as a function of the biochemical profile and the new profile using at least an analysis process, identifying a nutritional deficiency as a function of the corporeal variation, and producing the self-based nutritional program as a function of the nutritional deficiency, and initiate at least manufacturing of an edible combination as a function of the at least self-based nutritional program.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A system and methods for presenting an ailment from a modified nourishment scheme, the system comprising a computing device configured to identify a nutrition requirement for a user, receive, from a monitoring device, a monitoring element, generate a modified nutrition requirement as a function of the monitoring element, and identify an ailment that fulfills the modified nutrition requirement wherein identifying the ailment comprises determining a nourishment value corresponding to the modified nutrition requirement, and blocking nourishment contained within the modified nutrition requirement as a function of the nourishment value.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A system for generating a thyroid malady nourishment program includes a computing device, the computing device configured to obtain a vigor element, identify a thyroid status as a function of the vigor element, wherein producing the thyroid status further comprises obtaining a homeostatic element from a vigor database, producing a thyroid enumeration as a function of the vigor element, and identifying the thyroid status indicating thyroid medication status as a function of the homeostatic element and the thyroid enumeration using a status machine-learning model, determine an edible as a function of the thyroid status indicating thyroid medication status, and generate a nourishment program as a function of the edible.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Goods & Services

Matching consumers with professionals in the field of health, wellness, longevity, and optimal living Providing temporary use of online non-downloadable chatbot software using artificial intelligence (AI) for matching users with professionals in the field of health, wellness, longevity, and optimal living; Providing temporary use of online non-downloadable computer chatbot software for simulating conversations; Software as a service (SAAS) services featuring software for matching users with professionals in the field of health, wellness, longevity, and optimal living; Software as a service (SAAS) services featuring software using artificial intelligence (AI) for using user entered data to match users with professionals in the field of health, wellness, longevity and optimal living

Goods & Services

Software as a service (SAAS) services featuring software using artificial intelligence (AI) for users to enter personal health information, generating a status report based on that information with the ability to track information and generate reports each time new information is entered

Abstract

A method whereby a user is granted access to a digital content distribution platform subsequent to the user downloading a computer application onto a client device. Granting access includes providing the user with a line-of-credit usable by the user to access digital content files from the digital content distribution platform. The method includes receiving, from the client device, a user interaction with a social networking platform and increasing the line-of-credit as a function of the user's interaction with the social networking platform. The method includes receiving, from the user, a selection of a digital content file located on the digital content distribution platform and providing the user with access to the selected digital content file if a limit of the line-of-credit exceeds an associated cost of the selected digital content file, and then decreasing the line-of-credit by at least a portion of the line-of-credit.

IPC Classes  ?

• G06Q 20/24 - Credit schemes, i.e. "pay after"
• G06F 21/10 - Protecting distributed programs or content, e.g. vending or licensing of copyrighted material
• G06Q 20/08 - Payment architectures
• H04L 67/60 - Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources

Abstract

An artificial intelligence advisory system for vibrant constitutional guidance, the system comprising a computing device; an advisory module operating on the computing device, configured to receive at least a user input from a user client device; evaluate the content of the at least a user input to identify a theme of conversation; and locate an advisor client device operated by an informed advisor utilizing the identified theme of conversation; and an artificial intelligence advisor operating on the computing device, wherein the artificial intelligence advisor is configured to match the theme of conversation to a repository response utilizing a first machine-learning process, wherein the repository response contains a plurality of textual outputs related to the theme of conversation; select a textual output contained within the repository response; modify the textual output in response to the at least a user input; transmit the modified textual output to the user client device.

IPC Classes  ?

• G06F 16/332 - Query formulation
• G06F 18/23 - Clustering techniques
• G06F 18/24 - Classification techniques
• G06F 40/205 - Parsing
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/778 - Active pattern-learning, e.g. online learning of image or video features
• G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands

Abstract

An apparatus includes an optical scanning system; at least a processor; and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to generate, using the optical scanning system, a first scan of a plate; determine, using a machine vision system, an ingredient usage datum as a function of the first scan; determine a stored ingredient level as a function of the ingredient usage datum; and determine a stored ingredient addition as a function of the stored ingredient level.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06K 7/10 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation
• G06Q 50/12 - Hotels or restaurants

Abstract

A system for generating a stress disorder ration program, the system comprising a computing device, the computing device configured to obtain a stress representation; ascertain an equanimity signature, wherein ascertaining the equanimity signature further comprises retrieving an acclimation element; determining a relative vector as a function of the acclimation element; and ascertaining the equanimity signature as a function of the relative vector and the stress representation using a stress machine-learning model, and wherein the stress machine-learning model inputs the relative vector and the stress representation and outputs the equanimity signature; identify a physiological influence as a function of the equanimity signature; determine an edible as a function of the physiological influence; and generate a ration program as a function of the edible.

IPC Classes  ?

• A61B 5/16 - Devices for psychotechnicsTesting reaction times
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16H 20/70 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A system for generating a procreant functional program, the system comprising a computing device, the computing device configured to obtain a procreant marker as a function of a procreant system; determine a procreant appraisal as a function of the procreant marker; receive a conduct indicator; identify a functional signature as a function of the conduct indicator; and generate a functional program as a function of the functional signature and procreant appraisal using a program machine-learning model.

IPC Classes  ?

• G06N 20/00 - Machine learning
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance

Abstract

A system for telemedicine diagnostics through remote sensing may include at least a processor, and a memory communicatively connected to the at least processor, wherein the memory contains instructions configuring the at least processor to communicate to a subject a prompt, receive from the subject a response, classify the response to a medical condition category, generate a medical condition report as a function of the medical condition category, and initiate a telemedicine session by connecting the subject with a medical professional as a function of the response, and transmitting the medical condition report to a device operated by the medical professional.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
• G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
• G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
• H04L 65/65 - Network streaming protocols, e.g. real-time transport protocol [RTP] or real-time control protocol [RTCP]

Abstract

A system for generating physical activity sets for a human subject, the system comprising a computing device configured to detect a signal from a wearable fitness device, determine an activity profile using an activity machine-learning model and the signal, generate using the activity profile, at least a physical activity set, wherein generating further comprising classifying the activity profile with corresponding activity profile data from a plurality of human subjects, wherein the corresponding activity profile data correlates activity profile data to at least a physical activity set, training a fitness machine-learning model using the corresponding activity profile data, generating the at least a physical activity set as a function of the activity profile data and the fitness machine-learning model, and generate a presentation of the activity profile and the at least a physical activity set using a graphical user interface.

IPC Classes  ?

• G06N 20/00 - Machine learning
• A61B 5/11 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
• A63B 24/00 - Electric or electronic controls for exercising apparatus of groups
• A63B 71/06 - Indicating or scoring devices for games or players
• G09B 19/00 - Teaching not covered by other main groups of this subclass
• G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
• G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A system for an artificial intelligence alimentary professional support network for vibrant constitutional guidance includes a computing device. The system includes a diagnostic engine designed and configured to receive a biological extraction from a user and generate a diagnostic output based on the biological extraction. The system includes an advisor module designed and configured to receive a request for an advisory input, generate an advisory output using the request for an advisory input and the diagnostic output, and transmit the advisory output. The system includes an alimentary input module designed and configured to receive the advisory output, select an informed advisor alimentary professional client device as a function of the request for an advisory input, and transmit the at least an advisory output to the informed advisor alimentary professional client device.

IPC Classes  ?

• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G06F 16/906 - ClusteringClassification
• G06N 20/20 - Ensemble learning
• G16B 50/30 - Data warehousingComputing architectures
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Abstract

Described herein are systems and methods for modifying a physical transfer path. A system may include a computing device configured to generate an initial physical transfer path; receive a second request for a second alimentary combination; and generate a first modified physical transfer path by determining a second alimentary combination source location as a function of the second request; determining a first delivery time threshold as a function of the first request and a first spoilage threshold associated with the first alimentary combination; determining a second delivery time threshold as a function of the second request and a second spoilage threshold associated with the second alimentary combination; and generating the first modified physical transfer path as a function of the first delivery time threshold, the second delivery time threshold, and the second alimentary combination source location.

IPC Classes  ?

• G06Q 10/0835 - Relationships between shipper or supplier and carriers
• G06Q 10/047 - Optimisation of routes or paths, e.g. travelling salesman problem
• G06Q 10/0832 - Special goods or special handling procedures, e.g. handling of hazardous or fragile goods
• G06Q 10/0833 - Tracking

Abstract

A system for analyzing a nutritional content of an alimentary combination and a method related thereto include a processor and a memory communicatively connected to the processor, wherein the memory contains instructions configuring the processor to receive an input from a user device, wherein the input comprises an alimentary combination comprising at least a food-related ailment and at least a preference, compute a plurality of alimentary combination factors as a function of the input and a first machine-learning process, generate at least a modification pertaining to at least an alimentary combination factor of the plurality of alimentary combination factors using a second machine learning process, and output the at least a modification.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets

Abstract

An apparatus for generating alimentary data within a geofence is disclosed. The apparatus comprises a memory and at least a processor. The memory instructs the at least a processor to receive a geofence, identify population data as a function of the geofence, calculate a demographic conicity index as a function of the statistical makeup data, calculate an energy score as a function of the statistical makeup data, generate alimentary data as a function of a demographic conicity index, determine a plurality of phenotype clusters within the geofence as a function of the alimentary data, generate an alimentary program as a function of the alimentary data and the plurality of phenotype clusters, and identify one or more replacement ingredients within the ingredient combination, wherein the one or more replacement ingredients are nutritionally similar to the ingredients prescribed within the ingredient combination.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06N 20/00 - Machine learning

Abstract

A system generating a directional response is disclosed. The system comprises a computing device configured to receive a directional inquiry from a device operated by a user. Computing device is configured to retrieve a biological extraction from the user and generate a directional response by training a machine-learning process using directional training data correlating a plurality of biological extractions to a plurality of directions and generating the directional response as a function of the biological extraction from the user and the machine-learning process. Computing device is configured to update the directional response as a function of the preferences of the use and output the updated directional response to the device operated by the user. A method for generating a directional response is also disclosed.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06F 40/279 - Recognition of textual entities
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Abstract

A system for generating a cognitive disorder nourishment program and a method related thereto include a processor and a memory communicatively connected to the processor, wherein the memory contains instructions configuring the processor to obtain at least a cognitive indicator element comprising at least a biomarker and at least a user behavior, determine an edible as a function of the at least a cognitive indicator element, and generate a nourishment program as a function of the edible.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
• G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Abstract

An artificial intelligence system for generating educational inquiry responses from biological extractions and methods related thereto include a computing device designed and configured to retrieve a biological extraction pertaining to a user, identify, based on the biological extraction, at least a nutritional need of the user, receive at least an educational inquiry including a nutrition-related educational inquiry, select, based on the at least an educational inquiry, at least a machine-learning model, wherein selecting the at least a machine-learning model includes receiving biological extraction training data correlating exemplary biological extractions to exemplary nutritional support resources and training the at least a machine-learning model using the biological extraction training data, and generate, using the at least a machine-learning model and the at least a nutritional need of the user, an inquiry response including a plurality of nutritional support resources.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06N 20/00 - Machine learning

Abstract

A system for calculating a score for an edible in a display interface and methods related thereto include a sensor configured to detect user data comprising expanded biological extraction data and a computing device communicatively connected to the sensor, wherein the computing device is configured to generate a query as a function of the detected user data, identify a plurality of available edibles for one or more users as a function of the query, receive nourishment information relating to the plurality of available edibles, generate, for each available edible of the plurality of available edibles, a score as a function of expanded biological extraction data using a trained scoring machine-learning process, and display the score within a display interface.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06N 20/00 - Machine learning

Abstract

A system for generating a dietary instruction set using artificial intelligence and a method related thereto include a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to receive training data, record expanded biological extraction data from a first user, the expanded biological extraction data including physiological state data and at least a user behavior, generate a diagnostic output based on the expanded biological extraction data using at least a machine-learning algorithm iteratively trained as a function of the training data, and generate a dietary instruction set associated with the user as a function of the expanded biological extraction data and the diagnostic output, the dietary instruction set including at least structured meal plan.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06F 16/2455 - Query execution

Abstract

A system for arranging and displaying guided recommendations using a graphical user interface based on biological extraction, the system comprising a computing device configured to receive, from a wearable device located at a user, biological extraction data and at least a datum of user activity data, classify the biological extraction and the at least a datum of user activity as a function of at least a datum of a user fingerprint, select at least a compatible element as a function of the training data and the user fingerprint, wherein the compatible element comprises a guided recommendation, and generate a representation using a graphical user interface of the compatible element.

IPC Classes  ?

• G06F 17/00 - Digital computing or data processing equipment or methods, specially adapted for specific functions
• G06F 9/451 - Execution arrangements for user interfaces
• G06F 16/2457 - Query processing with adaptation to user needs
• G06F 16/248 - Presentation of query results
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning

Abstract

A system for generating a vibrant compatibility plan using artificial intelligence, comprising a server configured to receive at least a composition datum from a user client device wherein the at least a composition datum includes at least an element of user body data and at least an element of desired dietary state data, select at least a correlated dataset, create at least an unsupervised machine-learning model including at least a hierarchical clustering model to output at least a compatible food element, generate at least a vibrant compatibility plan wherein the at least a vibrant compatibility plan further comprises a plurality of compatible food elements each containing at least a food element compatibility index value score as a function of the at least a hierarchical clustering model.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A system for path selection, the system comprising a computing device, wherein the computing device is configured to receive a plurality of alimentary elements and a plurality of destinations. Computing device may compute, using the plurality of alimentary elements and the plurality of destinations, a projected combination as a function of an objective function, wherein computing is based on completion time and destination. Computing device may determine a combination ranking by generating a batching objective function, wherein the function generates an output ranking according to at least a target criterion and selects a combination. Computing device may provide batching instructions to a user. Computing device may determine a predicted path for the plurality of alimentary elements wherein the predicted path is updated as a function of each alimentary element that has reached its destination. Computing device may provide the predicted path to a user.

IPC Classes  ?

• G01C 21/34 - Route searchingRoute guidance

Abstract

A system for generating an alimentary plan is disclosed. The system comprises a computing device which is configured to receive an input that includes physiological data related to a skin sample. Computing device is configured to extract a plurality of biological indicators related to disease state from the physiological data. Computing device is configured to determine a biological indicator score for each biological score for each biological indicator of the plurality of biological indicators. Computing device is configured to generate a skin disorder classifier by receiving skin disorder training data. The computing device is configured to classify, using the skin disorder classifier, the at least one biological indicator and the biological indicator score to a positive result for a skin disorder. Computing device is configured to generate an alimentary plan as a function of the positive result. A method for generating an alimentary plan is also disclosed.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• A61B 10/02 - Instruments for taking cell samples or for biopsy
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• G16B 50/00 - ICT programming tools or database systems specially adapted for bioinformatics
• G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• G16H 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies

Abstract

Described herein are systems and methods for determining a compatible substance. In some embodiments, a system may include a camera; a user interface; and a computing device configured to, using the camera, capture a first image; generate a first body measurement by training a body measurement machine learning model on a training dataset including a plurality of example images correlated to a plurality of example body measurements; and generating the first body measurement as a function of the first image using the trained body measurement machine learning model; determine a first compatible substance as a function of the first body measurement; and using the user interface, display the first compatible substance.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06T 7/00 - Image analysis
• G06T 13/40 - 3D [Three Dimensional] animation of characters, e.g. humans, animals or virtual beings
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment

Abstract

A system method for selecting an intervention based on effective age, the system including at least a server, the at least a server designed and configured to obtain user data, project an actuarial life expectancy using a user chronological age, determine a telomeric age factor of the user by calculating a variance between the actuarial life expectancy and a projected actual mortality date, calculate an effective age of the user based on the telomeric age factor, and select an intervention from a plurality of configured to reduce the effective age.

IPC Classes  ?

• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing
• G01N 33/74 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving hormones
• G06F 17/18 - Complex mathematical operations for evaluating statistical data
• G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
• G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment

Abstract

A system for informing cooking style decisions, the system includes a computing device designed and configured to receive a personal recipe relating to a user. The computing device also designed and configured to select a cooking style, including locating in a recipe database a set of cooking styles associated with the personal recipe and selecting the cooking style from the set of cooking styles as a function of the user's biological extraction. Additionally, the computing device is designed and configured to incorporate the cooking style into the personal recipe of the user.

IPC Classes  ?

• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06F 16/9035 - Filtering based on additional data, e.g. user or group profiles
• G06F 40/237 - Lexical tools
• G06F 40/30 - Semantic analysis
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets

Abstract

An artificial intelligence system for behavioral pattern matching and language generation includes at least a server. The system includes a behavior modification module operating on the at least a server, wherein the behavior modification module is designed and configured to receive at least a request for a behavior modification and generate a behavior modification model as a function of the at least a request for behavior modification. The system includes an artificial intelligence advisor operating on the at least a server, wherein the artificial intelligence advisor is configured to receive at least a user input from a user client device, generate at least a textual output using the behavior modification model and the at least a user input, and transmit the at least a textual output to the user client device.

IPC Classes  ?

• G06N 5/02 - Knowledge representationSymbolic representation
• G06F 16/33 - Querying
• G06N 20/00 - Machine learning

Abstract

An apparatus and method for loop plating, the apparatus including a scanning system, at least a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to generate, using an optical scanning system, a first scan of a plate digitally connected to a user profile, track content data of the plate as function of the first scan, classify the content data to the user profile, wherein classifying the content data includes generating a loop assessment, verify the content data as function of the classification, generate, using the optical scanning system, a second scan of the plate, identify plating feedback as a function of the second scan, and update the user profile based on the plating feedback, wherein updating the user profile includes generating a progress report.

IPC Classes  ?

• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 20/68 - Food, e.g. fruit or vegetables
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets

Abstract

The present disclosure is generally directed to an apparatus for using a feedback loop to optimize meals, may include at least a processor; and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to retrieve nutrition data from a database. The processor may be configured to generate an optimization score, wherein generating the optimization score may include training an optimization machine-learning model, wherein the optimization machine-learning model is trained with optimization training data, inputting a nutrient quantity to the optimization machine-learning model to output a target nutrient score, and generating an optimization score as a function of the nutrition data and the target nutrient score.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06F 40/40 - Processing or translation of natural language

Abstract

The present disclosure is generally directed to an apparatus and method for outputting an alimentary program. The apparatus may include at least a processor, and a memory communicatively connected to the processor. The memory may contain instructions for configuring the at least a processor to iteratively receive user data from a plurality of remote devices, query the user data for a physical attribute of the user and a nutritional history of the user, classify the user data to the one or more phenotypic clusters, assign the classified user data one or more cohort labels as a function of the one or more phenotypic clusters, generate alimentary data as a function of the one or more cohort labels, and output an alimentary program to the user as a function of the alimentary data.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Abstract

A system for generating a pulmonary dysfunction nourishment program including computing device configured to receive at least a respiratory volume collection relating to a user, wherein the at least respiratory volume collection includes at least a biomarker, wherein the at least biomarker includes a bioremnant and a volatile organic compound (VOC), receive a salubrious reference relating to the user, identify a functional signature as a function of the salubrious reference, wherein the identifying further includes receiving a conduct indicator wherein the conduct indicator includes an indicator index, generating one or more conduct parameters as function of the salubrious reference, comparing the conduct indicator to the one or more conduct parameters and identifying the functional signature as a function of the comparison, and generate a nourishment program as a function of the functional signature.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation

Abstract

Described herein are systems and methods for generating a secure communication channel interface. Such a system may include a computing device configured to transmit, to a user client device, a configuration packet uniquely identifying the computing device; receive, from the user client device, an authentication datum; initiate a secure communication channel interface with the user client device as a function of the authentication datum; establish a security baseline parameter, wherein establishing the security baseline parameter includes identifying a network parameter; identifying a baseline audiovisual measurement; identifying a biometric identifier of a user; and identifying a user environment landmark comprising a physical object; identify a user health datum as a function of the security baseline parameter; share a health record of the user health datum using the secure communication channel interface; and transcribe audio information of the user health datum transmitted over the secure communication channel interface.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• H04L 65/1069 - Session establishment or de-establishment
• H04L 65/60 - Network streaming of media packets
• H04L 67/52 - Network services specially adapted for the location of the user terminal

Abstract

A system for informing product decisions including a computing device configured to receive a conditional complaint relating to a user select one or more articles of interest intended to correct the conditional complaint, classify the articles of interest to generate a tolerability score and display the tolerability score for each of the articles of interest.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06F 18/23 - Clustering techniques
• G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
• G06F 18/2433 - Single-class perspective, e.g. one-against-all classificationNovelty detectionOutlier detection
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G06N 20/00 - Machine learning
• G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment

Abstract

In an aspect, an apparatus for scoring a nutrient is presented. An apparatus may include at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor to receive user data from a user. At least a processor classifies a user to a profile cluster as a function of user data. At least a processor assigns the user one or more cohort labels as a function of the user data. At least a processor receives edible data. At least a processor extracts, from edible data, at least a nutrient. At least a processor scores at least a nutrient as a function of a profile cluster of a user.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Abstract

An apparatus for determining a conditional profile adjustment datum may include at least a processor; and a memory connectively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive a first plurality of photographs related to a human subject; identify a first conditional indicator as a function of the first plurality of photographs and entries contained within an expert database; generate a first conditional profile by training a classifier on a training dataset including a plurality of example conditional indicators as inputs correlated to a plurality of example conditional profiles as outputs; and generating the first conditional profile as a function of the first conditional indicator using the trained classifier; determine a conditional profile adjustment datum as a function of the first conditional profile; and communicate the conditional profile adjustment datum to the human subject.

IPC Classes  ?

• G06T 7/00 - Image analysis
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning

Abstract

A system for customizing informed advisor pairings, the system including a computing device. The computing device is configured to identify a user feature wherein the user feature contains a user biological extraction. The computing device is configured to generate using element training data and using a first machine-learning algorithm a first machine-learning model that outputs advisor elements. The computing device receives an informed advisor element relating to an informed advisor. The computing device determines using output advisor elements whether an informed advisor is compatible for a user.

IPC Classes  ?

• G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Abstract

A system for utilizing diagnostics for informed vibrant constitutional guidance is disclosed. The system includes at least a server and a diagnostic engine operating on the at least a server. The diagnostic engine is configured to receive a first set of contextual information associated with a user. The diagnostic engine is also configured to generate a diagnostic output as a function of the plurality of contextual information using a trained machine-learning model. The machine-learning model may be trained using the first training data set and the second training data set. The generating the diagnostic output includes transmitting the diagnostic output to an advisor client device.

IPC Classes  ?

• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• G06N 20/00 - Machine learning
• G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A system for selecting an ameliorative output using artificial intelligence includes at least a server configured to receive at least a prognostic output. At least a server is configured to generate a plurality of ameliorative outputs as a function of at least a prognostic output wherein the plurality of ameliorative outputs include at least a short-term indicator and at least a long-term indicator. At least a server is configured to receive at least a user life element datum wherein the at least a user life element datum includes at least a user life quality response. At least a server is configured to generate a loss function of the plurality of short-term indicators and the plurality of long-term indicators using at least a user life element datum. At least a server is configured to select at least an ameliorative output from a plurality of ameliorative outputs to minimize the loss function.

IPC Classes  ?

• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Abstract

Described herein are systems and methods of generating a food compatibility datum. In some embodiments, a system may include a computing device configured to receive a plurality of physiological extractions of a subject, wherein the plurality of physiological extractions comprises at least an inflammation metric; receive a plurality of alimentary element consumption data wherein each alimentary element consumption datum of the plurality of plurality of alimentary element consumption data describes a consumption of the subject prior to a physiological extraction of the plurality of physiological extractions; generate a plurality of alimentary element compatibility data; identify an inflammatory alimentary element as a function of the plurality of alimentary element compatibility data; pair a medical professional with the subject as a function of the inflammatory alimentary element; and display the inflammatory alimentary element using a user interface at a display device.

IPC Classes  ?

• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• A61B 90/00 - Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups , e.g. for luxation treatment or for protecting wound edges
• G06N 20/00 - Machine learning
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation

Abstract

A system for customizing treatments. The system includes a computing device configured to record a user biological extraction containing an element of user physiological data. The computing device is configured to receive condition state training data and generate a condition state model utilizing a first machine-learning algorithm. The computing device is configured to calculate a condition state label using the condition state model. The computing device is configured to select a treatment model utilizing the condition state label. The computing device is configured to generate a treatment model and output a plurality of treatments utilizing the treatment model.

IPC Classes  ?

• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A system and method for timing impact of nourishment consumption, the system including a computing device configured to receive training data, train a nutrient machine-learning model using the training data, generate a nutrient profile of the subject utilizing the nutrient machine-learning model, determine, using the nutrient profile, a nourishment consumption program, provide, to the subject, the nourishment consumption program, receive a set of nutrition consumption data of the subject as a function of the nourishment consumption program, and generate an updated nutrient profile as a function of the set of nutrition consumption data and the defined time intervals. The computing device further configured to generate audiovisual notifications based on the nutrient consumption program The computing device further configured to provide an updated consumption pattern of the nourishment consumption program as a function of the set of nutrition consumption data and the updated nutrient profile at defined time intervals.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06N 20/00 - Machine learning

Abstract

A system for determining a prescriptive therapy instruction set may include a computing device configured to receive a first subject prescription datum associated with a first subject; receive a first subject tolerance datum associated with the first subject; determine a prescriptive therapy instruction set by training a prescriptive therapy instruction set machine learning model on a training dataset including a plurality of example subject prescription data and subject tolerance data as inputs correlated to a plurality of example prescriptive therapy instruction sets as outputs; and generating the prescriptive therapy instruction set as a function of the subject prescription datum and the subject tolerance datum using the trained prescriptive therapy instruction set machine learning model; receive a second subject tolerance datum associated with the first subject; and retrain the prescriptive therapy instruction set machine learning model as a function of the second subject tolerance datum.

IPC Classes  ?

• G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Abstract

A system and method for generating a neonatal disorder nourishment program comprising a computing device, the computing device configured to obtain a neonatal indicator element, identify a neonatal bundle as a function of the neonatal indicator element, produce a neonatal profile as a function of the neonatal bundle, wherein producing further comprises obtaining a neonatal functional goal as a function of the neonatal bundle, receiving a neonatal recommendation as a function of a neonatal database, and producing the neonatal profile as a function of the neonatal functional goal and neonatal recommendation using a neonatal machine-learning model, determine an aliment as a function of the neonatal profile, and generate a nourishment program as a function of the aliment.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

An apparatus for generating alimentary data within a geofence is disclosed. The apparatus comprises a memory and at least a processor. The memory instructs the at least a processor to receive a geofence, wherein the geofence comprises a predetermined geographic area. memory instructs the at least a processor to identify population data as a function of the geofence, wherein the population data comprises at least a statistical markup data. The memory then instructs the processor to calculate an conicity index as a function of the at least a statistical markup data. The memory instructs the processor to generate alimentary data as function of the conicity index, wherein the alimentary data comprises a recommended nutrient intake. The memory then instructs the processor to determine a plurality of phenotype clusters within the geofence as a function of the alimentary data.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Abstract

In an aspect, an apparatus for generating an ingredient chain is presented. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor receive recipe data from a user. At least a processor is configured to extract a plurality of ingredients from recipe data. At least a processor is configured to classify, utilizing an ingredient classifier, a plurality of ingredients to a plurality of impact factors. At least a processor is configured to generate, as a function of impact factors, an ingredient chain for a user.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06N 20/00 - Machine learning

Abstract

A system for generating a gestational disorder nourishment program comprising a computing device, the computing device configured to obtain a maternal marker, calculate a gestational phase as a function of the maternal marker, wherein calculating the gestational phase further comprises, identifying a gestational goal, and calculating the gestational phase as a function of the maternal marker and the gestational goal as a function of a gestational machine-learning model, determine an edible as a function of the gestational phase, and generate a nourishment program as a function of the edible.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Described herein is a system for determining a physical combination instruction set, which may include a computing device configured to receive a first order for a first alimentary item from a remote device operated by a user; determine an environmental impact estimate as a function of the first order; transmit a signal to the remote device configuring the remote device to display the environmental impact estimate to the user; receive a delivery mode datum from the remote device; determine a physical transfer instruction set as a function of the delivery mode datum, wherein determining the physical transfer instruction set includes determining an area for additional users to submit orders for alimentary items to a cluster; and receiving, as a function of the area, orders for alimentary items; and transmit the physical transfer instruction set to a physical transfer apparatus.

IPC Classes  ?

• G06F 9/38 - Concurrent instruction execution, e.g. pipeline or look ahead
• G06N 20/00 - Machine learning

Abstract

A system for alimentary provisioning may include a computing device configured to record at least a biological extraction from a user; generate an alimentary instruction set for the user as a function of the at least a biological extraction; receive a goal parameter from the user; generate a plurality of ingredient combinations, wherein each ingredient combination is a combination of at least two ingredients of a plurality of ingredients; filter the plurality of ingredient combinations according to the goal parameter; and select a plurality of beneficial ingredient combinations for the user from the plurality of ingredient combinations, wherein the plurality of beneficial ingredient combinations is selected as a function of the alimentary instruction set.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06N 20/00 - Machine learning
• G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation

Abstract

A system for a physiologically informed virtual support network includes a support module operating on a computing device, the support module configured to receive a biological extraction having an element of user physiological data, generate a request for a user to join a support network as a function of the biological extraction, identify a support network for the user from a plurality of support networks, as a function of the biological extraction, and display to the user on the computing device, the identified support network, and a machine-learning module operating on the computing device and configured to assess a membership of the plurality of support networks, organize member participants of the plurality of support networks utilizing a first machine-learning process assign member participants to the plurality of support networks as a function of the first machine-learning process and select a support network leader for each support network.

IPC Classes  ?

• G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
• G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment

Abstract

A system for calculating a score for an edible in a display interface, the system having a computing device configured to determine an edible of interest relating to a user, receive nourishment information relating to the edible of interest to the user, wherein the nourishment information includes a plurality of ingredients, calculate one or more nutrient biodiversity scores as a function of the nourishment information including evaluating each ingredient of the plurality of ingredients, wherein evaluating each ingredient includes, extracting at least a nutrient from each ingredient of the plurality of ingredients and calculating a nutrient biodiversity score for the at least a nutrient, determine a nutritional requirement as a function of at least the nourishment information, and display the nutritional requirement and the one or more nutrient biodiversity scores of the edible of interest through a display interface.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets

Abstract

A system and method for nourishment refinement using psychiatric markers including a computing device designed and configured to retrieve a psychiatric marker relating to a user, wherein the psychiatric marker includes a physical measurement obtained from physiological extraction including a deoxyribonucleic acid related sample indicative of psychiatric condition, and a subjective response indicative of a user's current emotional state related to a psychological energy level, identify a nutrient variation as a function of the psychiatric marker, wherein identifying further includes determining a degree of psychiatric impairment, establish a nourishment possibility as a function of the nutrient variation and the degree of psychiatric impairment, and generate a nourishment program by training a machine learning process as a function of a training set containing a plurality of psychiatric markers and nutrient variations as input correlated to a plurality of nourishment programs as output, and generating the nourishment program as a function of the psychiatric marker and the nourishment possibility, using the trained machine-learning process.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• A61B 5/16 - Devices for psychotechnicsTesting reaction times
• G06F 16/242 - Query formulation
• G06F 16/2457 - Query processing with adaptation to user needs
• G06N 20/00 - Machine learning
• G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 20/70 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Abstract

An apparatus for physiologically informed gestational inquiries is disclosed. The apparatus includes at least a processor and memory communicatively connected to the at least a processor. The memory instructs the processor to receive a biological extraction from the user. The memory instructs the processor to receive a gestational inquiry from the user. The memory instructs the processor to separate the gestational inquiry from a description of the gestational inquiry. The memory instructs the processor to determine a gestational target as a function of the gestational inquiry and the biological extraction. The memory instructs the processor to generate a gestational report as a function of the gestational target.

IPC Classes  ?

• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning
• G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

An apparatus and method for analyzing a communication datum in a metaverse. The apparatus may include at least a processor; and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to: receive a communication datum from a user in a metaverse related to the user; receive a user profile related to the user; identify a plurality of communication providers as a function of the communication datum; classify the communication datum to the user profile; and output a communication request to a communication provider device.

IPC Classes  ?

• G06Q 30/02 - MarketingPrice estimation or determinationFundraising
• G06N 20/00 - Machine learning

Abstract

A system for ordered food preferences accompanying symptomatic inputs, the system including a computing device, the computing device designed and configured to retrieve a food profile pertaining to a user; select a first food element as a function of the food profile; select a second food element as a function of the first food element; create a food preference menu wherein the food preference menu contains the first food element and the second food element; and modify the food preference menu as a function of an entry contained within a symptomatic database.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06F 16/2457 - Query processing with adaptation to user needs
• G06N 20/00 - Machine learning
• G06Q 10/0875 - Itemisation or classification of parts, supplies or services, e.g. bill of materials
• G06Q 30/0601 - Electronic shopping [e-shopping]
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies

Abstract

An apparatus for generating cryptographic keys associated with a biological extraction is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a plurality of biological extractions from a plurality of users. The memory instructs the processor to classify each of the plurality of biological extractions to a plurality of biological extraction clusters. The memory instructs the processor to generate an interconnection metric as a function of a comparison between the plurality of biological extraction clusters. The memory instructions the processor to generate an interconnection metric as a function of a comparison between the plurality of biological extraction clusters using a metric machine learning model. The memory instructs the processor to generate a cluster key associated with the interconnection metric and a biological extraction cluster of the plurality of biological extraction clusters.

IPC Classes  ?

• G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
• G06F 18/23 - Clustering techniques
• H04L 9/08 - Key distribution

Abstract

In an aspect, a system and method for developing a generating a circuit protocol for instituting a desired body mass index (BMI) including receiving at least a body mass index representation and a circuit record, generating at least a change of mode by receiving training data correlating mode elements to BMI representations, training a machine learning model as a function of a machine learning algorithm and the training data, and generating at least a change of mode as a function of the machine learning model, and the circuit record, and generating the circuit protocol as a function of the at least a change of nutrition.

IPC Classes  ?

• G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Abstract

A system for tracking clusters in an immutable sequential listing is disclosed. The system includes at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the processor to receive a new record, obtain a sub-listing of an immutable sequential listing, wherein the sub-listing comprises a biological extraction cluster description posting of a biological extraction cluster and a cluster key, update the biological extraction cluster description posting using the new record, generate a new sub-listing and update the new sub-listing to the immutable sequential listing.

IPC Classes  ?

• G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
• H04L 9/08 - Key distribution
• G06F 18/23 - Clustering techniques

Abstract

The present disclosure is generally directed to an apparatus for using a feedback loop to optimize meals, may include at least a processor; and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to retrieve nutrition data from a database. The processor may be configured to generate an optimization score, wherein generating the optimization score may include training an optimization machine-learning model, wherein the optimization machine-learning model is trained with optimization training data, inputting a nutrient quantity to the optimization machine-learning model to output a target nutrient score, and generating an optimization score as a function of the nutrition data and the target nutrient score.

IPC Classes  ?

• G09B 19/00 - Teaching not covered by other main groups of this subclass
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06N 20/00 - Machine learning

Abstract

In an aspect, an apparatus for generating an ingredient chain is presented. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor receive recipe data from a suer. At least a processor is configured to extract a plurality of ingredients from recipe data. At least a processor is configured to classify, utilizing an ingredient classifier, a plurality of ingredients to a plurality of impact factors. At least a processor is configured to generate, as a function of impact factors, an ingredient chain for a user.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06F 16/9538 - Presentation of query results

Abstract

In an aspect an apparatus for determining toxic load quantifiers is presented. An apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor to receive user input comprising user data. At least a processor is configured to generate a query as a function of user data. At least a processor is configured to obtain query results as a function of a query. At least a processor is configured to determine a toxic load quantifier of a query results of query results as a function of a toxicity criterion and user data. At least a processor is configured to display a toxic load quantifier of query results to a user.

IPC Classes  ?

• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• G06Q 30/0282 - Rating or review of business operators or products
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G06N 20/00 - Machine learning

Abstract

A system for identifying and ameliorating body degradations, the system comprising a computing device, wherein the computing device is configured to receive biological extraction data. Computing device may generate, as a function of a degradation machine-learning model and the biological extraction data, a degradation profile. Computing device may calculate a biological degradation function that is a mathematical function that describes the change in rate of degradation over time corresponding to the user. Computing device may identify, using a degradation imbalance machine-learning process and the degradation profile, a degradation imbalance. Computing device may determine, as a function of the degradation imbalance machine-learning process and the degradation imbalance, a degradation antidote strategy to decrease the rate of biological degradation of a user by performing a simulation. Computing device may display to a user the degradation antidote strategy and a degradation prevention instruction set for a user to alter degradation rates.

IPC Classes  ?

• G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Abstract

The present disclosure is generally directed to an apparatus for outputting at least a recipe to a user is described. The apparatus may include at least a processor, and a memory communicatively connected to the processor. The memory may contain instructions for configuring the at least a processor to receive user data from a user. The processor may be configured to classify the user data to a one or more phenotypic clusters and assign the user one or more cohort labels as a function of the one or more phenotypic clusters. Further, the processor may be configured to generate alimentary data as a function of the one or more phenotypic cluster and the one or more cohort labels. Moreover, the processor may be configured to output an alimentary program to the user.

IPC Classes  ?

• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations

Abstract

A system and method for classifying media according to user negative propensities is illustrated. The system includes a computing device configured to obtain a physiological state data as a function of a user input, identify a user propensity for problematic behavior associated with a human subject as a function of the physiological state data, wherein the user propensity for problematic behavior identifies an problematic behavior from a predetermined plurality of problematic behaviors, receive a media item containing a principal theme to be transmitted to a device operated by the human subject, and block transmission of the media item to the device operated by the human subject as a function of the principal theme and the user propensity for problematic behavior.

IPC Classes  ?

• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Abstract

A system for multi-factorial physiologically informed refreshment selection using artificial intelligence, the system comprising a computing device, the computing device designed and configured to retrieve a biological extraction pertaining a user, wherein the biological extraction contains an element of user data, select, a nutritional machine-learning model using the biological extraction, determine a geolocation of the user, identify a provider located within the geolocation of the user, wherein the provider generates a plurality of refreshment possibilities, determine the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine learning model wherein the compatibility comprises a numerical score associated with a tolerance of the user, and display the compatibility of the plurality of refreshment possibilities.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

A system for providing alimentary elements includes a computing device configured to receive a primary input relating to a first user for a compatible alimentary element based on biological extraction, receive secondary input from the second user for an alimentary element not associated with biological extraction, generate an extensible alimentary element display for the second user, wherein generating the extensible alimentary element display includes locating at least an alimentary element originator as a function of the secondary input and the compatible alimentary elements, generating a queue of alimentary elements from the located alimentary element originator, wherein the queue includes alimentary elements for the second user as a function of the secondary input and compatible alimentary elements for the first user, and provide a representation, via a graphical user interface, of a compatible alimentary element for the first user and an alimentary element for the second user.

IPC Classes  ?

• G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
• G06F 9/54 - Interprogram communication
• G16B 10/00 - ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
• G06N 20/00 - Machine learning
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets

Abstract

A system for using artificial intelligence to analyze user activity data, the system comprising a computing device configured to receive from a user, at least a biological extraction and at least a user activity datum, determine a current user location, generate a diagnostic output as a function of the biological extraction, wherein the diagnostic output comprises a condition of the user, retrieve, from a fingerprint database, at least a datum of user fingerprint data, identify a plurality of compatible elements at the current user location as a function of the condition of the user, select at least a compatible element as a function of the fingerprint data, and present, via a graphical user interface, the at least a compatible element to a user device.

IPC Classes  ?

• G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer
• G06N 20/00 - Machine learning
• H04L 67/30 - Profiles
• H04L 43/18 - Protocol analysers
• H04L 43/045 - Processing captured monitoring data, e.g. for logfile generation for graphical visualisation of monitoring data
• G06F 18/24 - Classification techniques
• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects

Abstract

A system for determining a prioritized instruction set for a user, the system comprising a computing device, wherein the computing device is configured to receive at least a physiological goal and provide a plurality of biological extraction data. Computing device may determine a user baseline profile using training data, wherein training data correlates biological extraction data and physiological goals to baseline profile elements, train a machine-learning model using the training data, and determine the user baseline profile as a function of the machine-learning model. Computing device may generate a differential action as a function of the user baseline profile and the physiological goal, receive a plurality of user preference data, and selecting the differential action from the plurality of candidate differential actions. Computing device may receive an updated biological extraction datum corresponding to the user and may modify the differential action as a function of the updated biological extraction datum.

IPC Classes  ?

• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G06N 20/20 - Ensemble learning
• G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets

Abstract

An apparatus and method for adjusting a user nourishment selection based on nutrient diversity, the apparatus comprising at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive a proposed user selection relating to nourishment, wherein the proposed user selection comprises a plurality of ingredients, evaluate each ingredient of the plurality of ingredients, wherein evaluating each ingredient includes extracting at least a nutrient from each ingredient of the plurality of ingredients and calculating a nutrient biodiversity score for the at least a nutrient, optimize the plurality of ingredients as a function of each nutrient biodiversity score, and adjust the plurality of ingredients as a function of the optimization of the plurality of ingredients.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment

Abstract

A method whereby a user is granted access to a digital content distribution platform subsequent to the user downloading a computer application onto a client device. Granting access includes providing the user with a line-of-credit usable by the user to access digital content files from the digital content distribution platform. The method includes receiving, from the client device, a user interaction with a social networking platform and increasing the line-of-credit as a function of the user's interaction with the social networking platform. The method includes receiving, from the user, a selection of a digital content file located on the digital content distribution platform and providing the user with access to the selected digital content file if a limit of the line-of-credit exceeds an associated cost of the selected digital content file, and then decreasing the line-of-credit by at least a portion of the line-of-credit.

IPC Classes  ?

• G06Q 20/24 - Credit schemes, i.e. "pay after"
• G06F 21/10 - Protecting distributed programs or content, e.g. vending or licensing of copyrighted material
• G06Q 20/08 - Payment architectures
• G06Q 20/38 - Payment protocolsDetails thereof
• H04L 67/60 - Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources

Abstract

A system for generating a nourishment program includes a computing device configured to retrieve a nociception parameter, classify the nociception parameter to a nociception grouping, identify, using the nociception grouping, a plurality of nutrition elements, wherein identifying the plurality of nutrition elements includes generating a plurality of nutritional metrics associated with reduction of nociception as a function of the nociception grouping, determining a respective effect of each nutritional metric of the plurality of nutritional metrics on the nociception parameter, calculating at least a nutritional level as a function of the respective effect of each nutritional metric, wherein the at least a nutritional level comprises an amount intended to address the nociception parameter, and identifying the plurality of nutrition elements as a function of the at least a nutritional level, and generate a nociception nourishment program using the plurality of nutrition elements.

IPC Classes  ?

• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets

Abstract

A system for physiologically informed therapeutic provisions includes a computing device configured to receive, from a remote device operated by a user, one or more conditional datum wherein the each of the one or more conditional datum contains a description of a current bodily complaint. The computing device is further configured to identify a plurality of antidotal therapeutic provisions as a function of the one or more conditional datum. The computing device is further configured to locate a user biological extraction wherein the user biological extraction contains at least an element of user physiological data. The computing device is further configured to generate a compatibility model, wherein the compatibility model utilizes the plurality of antidotal therapeutic provisions and the user biological extraction as an input and outputs one or more compatible antidotal therapeutic provisions and a corresponding custom instruction set for the one or more compatible antidotal therapeutic provisions.

IPC Classes  ?

• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A system and method for generating a parasitic infection program is disclosed. The system includes a computing device configured to receive parasitic background training data including a plurality of parasitic biomarkers as input correlated to a plurality of parasitic disease as output, train a parasitic background machine-learning model with the parasitic background training data, generate a parasitic background as a function of the parasitic background machine-learning model and at least a parasitic biomarker including a host factor, generate a parasitic disease assessment as a function of the parasitic background, which includes classifying the parasitic background into the parasitic disease assessment using an assessment classification machine-learning process and generate a parasitic infection program as a function of the parasitic disease assessment using a parasitic program model trained with parasitic program training data including a plurality of parasitic disease assessments as input correlated to a plurality of parasitic infection programs as output.

IPC Classes  ?

• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations

Abstract

In an aspect an apparatus for determining toxic load quantifiers is presented. An apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor to receive user input comprising user data. At least a processor is configured to generate a query as a function of user data. At least a processor is configured to obtain query results as a function of a query. At least a processor is configured to determine a toxic load quantifier of a query results of query results as a function of a toxicity criterion and user data. At least a processor is configured to display a toxic load quantifier of query results to a user.

IPC Classes  ?

• G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
• G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
• G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• G06N 20/00 - Machine learning
• G06Q 30/0282 - Rating or review of business operators or products